International Journal of Artificial Intelligence, Data Science, and Machine Learning
Agentic AI in Energy Operations: Transforming Efficiency and Sustainability
The world's Energy & Utilities (E&U) industries are being rapidly transformed digitally due to increased complexity in grids, rapid integration of renewables, constraints on the workforce, and increasing mandates to decarbonize. As such, traditional automation based on static rules, and pre-defined operating conditions, will be unable to manage the volatility and resiliency demands of today's energy systems. Agentic AI utilizing autonomous multi-agents capable of perceiving, reasoning, planning and acting together, presents an opportunity to transition to sustainable and self optimizing energy ecosystems. This paper integrates advances in multi-agent reinforcement learning (MARL), deep reinforcement learning (DRL) for microgrid optimization, distributed intelligence in the cloud-edge, and interpretable AI to provide a comprehensive operational framework for the implementation of Agentic AI in energy systems. Utilizing state-of-the art literature (2021–2026) we describe conceptual models which enable autonomous load balancing, renewable dispatch optimization, predictive maintenance and coordination of resilient microgrids during extreme events. Studies indicate that Agentic AI may result in lower OPEX, higher utilization of DER, better carbon performance and faster outage recovery. However, achieving these benefits requires stronger governance, robust cyber security protection and human-in-the-loop design. This paper concludes with a developmental road map, and future research priorities required to scale autonomous intelligence throughout global energy networks.
GenAI-Scenario Digital Twins for ERP-Backed Supply Networks
Global supply networks face increasing vulnerability to pandemics, geopolitical sanctions, cyber threats, and logistical disruptions. Traditional ERP systems offer transaction-level visibility but cannot simulate cascading failures or guide recovery actions. This paper presents a GenAI-Scenario Digital Twin (GSDT) framework that integrates BPMN, Knowledge Graphs, and Digital Twin pipelines for ERP-backed supply chains. The framework continuously ingests ERP event streams and external risk feeds to generate adaptive, policy-aware simulations. Using Generative AI-driven scenario stress tests, it evaluates resilience metrics such as fill rate, backlog days, and time-to-recover under disruptions including port closures, cyberattacks, and sanctions. Validation combines retrospective replay of historical events with prospective stress-testing experiments. The study contributes a reusable simulation toolkit, a resilience metrics catalog, and case study evaluations with industry and agency partners, providing a scalable approach to enhance supply network resilience.
GovGPT: An Ethics-Integrated Governance Architecture for Curriculum-Aligned, Child-Centric Educational AI Systems
This paper introduces GovGPT, a comprehensive governance architecture for educational AI systems that integrates ethical constraints directly into system design. Building upon prior work on curriculum alignment and hallucination mitigation, we formalize child-centric safety and pedagogical appropriateness as first-class architectural concerns. The framework operationalizes emerging IEEE standards (P7004/P7008) through a multi-layered governance stack comprising: (1) a Policy Interpretation Layer translating ethical guidelines to executable constraints, (2) a Curriculum Authority Layer enforcing syllabus boundaries via retrieval-augmented generation, (3) a Child-Safety Filtering Layer implementing age-appropriate content screening, and (4) an Audit Layer providing explainable compliance verification. We introduce formal definitions for governance failures in educational contexts, prove bounded deviation properties under policy constraints, and validate the architecture through simulated deployment scenarios. Experimental results demonstrate a 47% reduction in policy violations and 92% compliance with child-safety standards compared to baseline systems, while maintaining pedagogical efficacy. This work establishes a foundation for systematically trustworthy educational AI that balances generative capability with ethical responsibility.
Standardizing AI Agent Protocols: Definitions, Layered Architectures, and Future Research Directions
AI agents are moving from isolated task executors to networked, autonomous participants in software ecosystems. As a current member of the W3C AI Agent Protocol Community Group, I draw on ongoing standards discussions and emerging best practices to frame the interoperability and governance problem. As agents increasingly discover resources, invoke tools, negotiate tasks, and coordinate with humans and other agents, ad hoc interfaces fail to provide interoperability, security, and predictable behavior at scale. This article defines AI Agent Protocols as composable specifications spanning transport, message semantics, capability representation, policy enforcement, and governance. We synthesize recent surveys of agent protocols, standards efforts including the W3C AI Agent Protocol Community Group, and emerging interoperability layers such as the Model Context Protocol to propose a reference architecture and taxonomy. We analyze design tradeoffs, threat models, and conformance criteria, and identify research directions: semantic interoperability, verifiable identity, policy-carrying messages, adaptive coordination, and safety-by-construction for large-scale multi-agent systems.
Generative AI for Microfluidic Device Design
Microfluidic devices enable precise manipulation of fluids at the microscale and are widely used in biomedical diagnostics, drug delivery, and lab-on-a-chip systems. Traditional microfluidic device design relies heavily on iterative simulations and expert-driven trial-and-error processes, which are time-consuming and computationally expensive. Recent advances in generative artificial intelligence offer new opportunities to automate and optimize microfluidic design by learning complex flow patterns and geometries from data. This paper explores the application of generative AI models for microfluidic device design, including variational autoencoders, generative adversarial networks, and diffusion-based models. The study reviews design methodologies, simulation integration, performance evaluation, and emerging research directions. Results indicate that generative AI can significantly reduce design time while improving flow efficiency, mixing performance, and device robustness.
Privacy-Preserving Technologies in Edge Analytics: Architectures, Mechanisms, and Performance Frontiers
The shift of data analytics from centralized clouds to the network edge enhances latency and bandwidth efficiency, but it also raises significant privacy concerns. As sensitive biometric, behavioral, and operational data are gathered on resource-limited IoT devices, traditional security measures become outdated. This paper explores the main challenges in securing edge analytics, emphasizing the "Privacy-Utility Paradox" and the risks of inference attacks. It provides a detailed analysis of distributed learning architectures—Federated Learning (FL), Split Learning (SL), and Swarm Learning—and assesses cryptographic techniques such as Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMPC). Based on recent quantitative research, we propose mitigation strategies that balance accuracy, latency, and energy use, guiding the development of compliant and trustworthy edge intelligence systems.
Vibration and Acoustic Signal Analysis for Early Fault Detection in Clean-Room Robotics
Clean-room robotic systems play a crucial role in semiconductor manufacturing, pharmaceutical assembly, and precision laboratory settings. These robots must operate reliably to avoid costly production delays and product contamination. Early detection of mechanical faults through vibration and acoustic signal analysis can improve reliability, reduce maintenance costs, and prevent unplanned downtime. This paper reviews key methods for extracting vibration and acoustic features, signal processing techniques, diagnostic models, experimental results, and future research directions. Results show that frequency domain analysis, machine learning classifiers, and sensor fusion improve fault detection accuracy in robotic joints, motors, and bearings.
Bridging Text Embeddings for Unconventional Linguistic Contexts
This document explores a novel extension of word embedding techniques to facilitate contextual information extraction from atypical textual sources. Utilizing a corpus derived from the tvtropes dataset, this work introduces two distinct models: an N-Grams Permutation approach and a Database-Like Reinforcement methodology. A comparative evaluation demonstrates that the artificially generated corpus, when processed by these models, significantly enhances accuracy by up to 45.2% over human-curated linguistic representations for this specialized application of Natural Language Processing.
Beyond Automation: Achieving Coherent Intelligence with Shared Memory Architectures
Enterprise AI is rapidly shifting from single-model deployments to multi-agent systems that coordinate discovery, analysis, validation, and synthesis across complex workflows. Yet in practice, agent brilliance often collapses into collaboration chaos insights vanish, work repeats, and reasoning drifts. This paper advances a Shared Memory Architecture (SMA) for multi-agent AI that elevates memory from a feature to a foundational system design. By implementing a context graph with semantic embeddings, lineage, confidence scoring, and validation links, organizations can transform disconnected automations into coherent, auditable intelligence. The approach demonstrates measurable improvements reducing redundant compute, stabilizing reasoning, and enabling full decision traceability while aligning with regulatory and governance expectations. The paper outlines architecture, implementation patterns, governance guardrails, and adoption playbooks, with use cases spanning trading, retail, supply chain, and integrated quality engineering. The outcome: systems that remember, reason, and regulate themselves, unlocking scalable enterprise value under uncertainty.
AI-Augmented Compliance Monitoring for Enterprise Infrastructure
Enterprise infrastructures are growing in scale, complexity, and risk exposure especially in regulated industries. With hybrid, multi-cloud environments and increasingly distributed architectures, traditional compliance monitoring methods are no longer sufficient. Enter Artificial Intelligence (AI): a powerful enabler that can transform how organizations track, analyze, and enforce compliance. This paper explores the rise of AI-augmented compliance monitoring where machine learning, pattern recognition, and natural language processing work alongside humans to maintain real-time, predictive, and proactive compliance. We examine architectures, use cases, benefits, and risks. Through real-world examples and emerging tools, we reveal how AI can reduce audit burdens, detect anomalies, automate policy enforcement, and bridge the gap between compliance and agility in modern enterprise platforms.
Integration of Hyperledger-Based Distributed Ledger with Cloud-Native Microservices for Scalable Post-Trade Processing Solutions
Capital markets post-trade processes (trade capture, clearing, settlement and reconciliation) are currently limited by excessive data fragmentation, reconciliation lag and high operational expenses. A central architecture creates "data silos" which restricts scalability, transparency and flexibility of integration between disparate financial institutions. This paper introduces a unified, technically advanced framework integrating Hyperledger Fabric (HLF); a permissioned Distributed Ledger Technology (DLT) with cloud native micro services as a means of creating a scalable, fault-tolerant and transparent ecosystem. By implementing Kubernetes based orchestration and Istio service mesh, we have shown how a legacy monolithic system can be replaced with a dynamic, distributed system capable of supporting high frequency transactions. Simulation results on large scale cloud based test beds show that our predictive resource orchestration framework achieves a 5 times greater throughput than a typical standalone DLT deployment and a 26-fold reduction in 95th percentile (p95) latency. The framework offers a scalable way of provisioning AI driven FinTech workloads with significantly increased reliability and decreased Total Cost of Ownership (TCO).
Autonomous DevOps Pipelines Using Multi-Agent Systems for Enterprise-Grade Cloud Delivery
Traditional DevOps pipeline models are being stretched to their limits due to the increasing complexity and variability of cloud-native and multi-cloud environments. While many CI/CD pipelines have an automated process, they typically use a central orchestrator, rule-based systems, and manual intervention by humans. This makes them less capable of adapting to runtime uncertainty, cascade failures, and emerging security threats. This research will develop the concept of Autonomous DevOps Pipelines based on Multi-Agent Systems (MAS) as a foundation for large-scale cloud delivery across enterprises. The primary objective of this research is to explore the extent to which intelligent software agents can be used throughout the DevOps lifecycle to provide self-orchestration, adaptive decision making, and continuous optimization within changing operational environments. Using a design-oriented and conceptual research methodology, this paper brings together prior research on DevOps automation, Distributed Artificial Intelligence, and AIOps; it then unifies the findings in a reference architecture for a multi-agent system in DevOps. The proposed reference architecture defines the roles of agents, the processes through which agents will coordinate with each other, how agents will learn from interactions with their environment, and how agents will be governed in terms of policies that reflect the needs of an enterprise. The evaluation of the reference architecture demonstrates that MAS-based DevOps pipelines can improve deployment reliability, decrease mean time to recovery, and accelerate release cycles while maintaining security, regulatory compliance, and auditability. The paper also identifies significant research and engineering challenges associated with MAS-based DevOps pipelines including, but not limited to, the cost of coordinating between agents, the trustworthiness and explainability of autonomous agent decisions, and the management of operational risk. Ultimately, the evaluation of the reference architecture suggests that using multiple intelligent agents provides a viable and scalable means to enable self-healing, self-optimizing, and intelligent cloud delivery platforms that can support complex enterprise environments.
The conventional methods of financial document analysis have been based on official numerical pointers, without considering the huge narrative data in company reports, regulatory submissions, morale reports, or sector statements. According to the recent progress in the sphere of -informed large language models (LLMs), machines have become capable of processing and encoding financial documents with better accuracy than at any previous point in history. This paper presents a multimodal LLM architecture, which makes use of financial reports to synthesize a narrative and lock it in with a time-series predictor in external downstream trainers that do not use the narrative, namely forecasting and control. As the energy and insurance sectors view of case studies evidence, document processing through LLM does increase the accuracy of prediction in addition, that it makes reading or comprehension superior, contributing to risk identification, harmonization of the rules, and decision-making. The findings highlight the value of LLMs as a methodological roadmap for financial document processing beyond the banking sector.
Integration of Grok 3 AI Model in Oracle Cloud
The incorporation of new artificial intelligence frameworks into cloud solutions is transforming the future of enterprise computing. This paper discusses the implementation and integration of the Grok 3 AI model in Oracle Cloud, covering its features, architecture, and revolutionary role in cloud services. Grok 3, with its advanced capabilities in natural language understanding and data processing, enhances the value of Oracle Cloud by enabling smarter analytics, automated processes, and informed decision-making techniques. The integration addresses challenges of scalability, security, and interoperability, demonstrating how Grok 3 can empower businesses to harness AI-driven insights seamlessly. This analysis examines the technical implementation, use cases, and future trends in detail, providing organizations with a roadmap to leverage AI within Oracle’s robust cloud ecosystem.
Federated Learning with Smartphone Sensor Data
Federated learning (FL) is emerging as a promising approach for training machine learning models on distributed devices without violating the data privacy of these devices. In this paper, we examine federated learning for smartphone sensor data applications that involve both significant challenges related to privacy and data heterogeneity. Our primary interest lies in techniques such as differential privacy, secure aggregation, and homomorphic encryption that will ensure privacy over user-sensitive information during model training. We also pose and discuss heterogeneous data across devices, particularly non-independent and identically distributed (non-IID) data, by investigating methods such as normalization of data, personalized learning, and federated transfer learning. Using real-world smartphone sensor datasets, we demonstrate experimentally that federated learning is effective in training robust models while preserving privacy and accounting for device-specific data variations. Our findings highlight that federated learning can be regarded as a way to scale-up and privacy-preserve mobile-based machine learning, which may open new avenues for building real-time AI systems on top of devices themselves
Explainable Deep Learning Models for Early Diagnosis of Cardiovascular Diseases Using Multi-modal Patient Data
Deep learning and Artificial Intelligence have started showing their effectiveness in different public sectors, including healthcare, due to the swift advancement of digital technologies. Though they are integrated into the patient care activity, they are still challenged by the black-boxing of their working process. Several deep learning measures have been described that enforce early detection of cardiovascular diseases. Objectification of key factors like privacy and security of patients should be taken into consideration before the diagnosis approach so that clinical trust is achieved and healthcare systems become reliable on the automation algorithm. The study has considered the utilisation of a dataset consisting of 50 entries that help in understanding the rise of CVD through gender segmentation. The use of the CNN model has evaluated the fact that explainable deep learning is the epitome of accuracy, which has the capability of diagnosing the condition of cardiovascular disease in patients and assists healthcare professionals with better solutions to treat them. A proper normal distribution was observed through the tests called ANOVA, descriptive and correlation. The factors such as smoking, the lack of physical activity and limitation in education consecutively lead to the rise of CVD
Explainable Artificial Intelligence (XAI)
AI-Driven Quality Assurance Frameworks for Decentralized Clinical Trials
Traditional quality assurance (QA) processes used in electronic Clinical Outcome Assessment (eCOA), and Decentralized Clinical Trial (DCT) platforms rely on a variety of manual, reactive approaches with many regulatory risk elements. The purpose of this research is to introduce an AI-powered Predictive Quality Assurance (PQA) system that uses Ensemble Machine Learning Models to predict problems with the DCT platform or eCOA application prior to their release into production. Using simulated and anonymized QA data sets, the PQA system will implement Supervised Learning, Deep Learning and Hybrid models to generate Risk Scores at the Module Level as well as Optimize Regression Test Coverage in Real Time. The evaluation outcomes showed improvements in defect prediction accuracy and reductions in Defect Leakage post Release, and thus the use of AI-Driven QA can enhance the dependability of software applications, prepare the software applications for Regulatory Audits, and help enable the Digital Transformation of Clinical Research Systems.
Electronic Clinical Outcome Assessment (eCOA)
Adaptive Data Quality Management for Multi-Cloud Healthcare Warehouses: FHIR-Aware Semantics and Unsupervised Thresholding
The rapid proliferation of multi-cloud architectures in healthcare promises elastic scalability and regional redundancy, but it also introduces acute challenges in data consistency, latency, and governance. Traditional, centrally orchestrated, rule-based Data Quality Management (DQM) tools are ill-equipped to handle the volume, heterogeneity, and semantic complexity of distributed electronic health records (EHRs) and claims data. As schemas drift and new data sources are onboarded, static checks generate escalating false positives, incur avoidable data movement costs, and contribute to “data swamps” that compromise clinical decision-making. This paper presents an Adaptive Data Quality Management framework for multi-cloud healthcare warehouses that combines unsupervised anomaly detection with FHIR-aware semantic validation. The framework deploys lightweight quality components alongside analytic workloads to profile and score data streams, while a cloud-agnostic control layer dynamically adjusts quality thresholds using rolling statistics over anomaly scores. A FHIR-based semantic distance metric decomposes deviations into structural, vocabulary, and cardinality components, enabling graded policies rather than binary pass/fail checks. Using a synthetic but structurally realistic workload of approximately 500,000 patients generated by a Synthea-style engine and partitioned across AWS, Azure, and GCP, we evaluate the framework under controlled “chaos engineering” scenarios including schema drift, value-set drift, and volume anomalies. Compared with a centralized, rule-based DQM baseline, the adaptive framework reduces false-positive quality alerts by roughly 40% while increasing precision from about 0.62 to 0.74 at comparable recall. These results demonstrate that combining FHIR-aware semantics with unsupervised, adaptively thresholded quality scoring can substantially reduce noise in quality monitoring while preserving anomaly detection performance in multi-cloud healthcare analytics and decision-support systems
The Role of Machine Learning in Early Detection of Chronic Diseases through Wearable Data
In this study, an insight into the various methods utilised by medical practice to facilitate patient diagnostics of early detection of chronic diseases using concepts of machine learning. One of the major implications that has been presented in the study includes a variety of models that enable the application of machine learning in different areas of disease detection, concerning the factors of accuracy and precision. The complexities that lie within the utilisation of such models have been evaluated based on secondary data and an extensive literature review such that its feasibility in healthcare context can be studied effectively. The methods applied to study the role of machine learning include case studies and scholarly articles that provide better insights into research that has been carried out in the medical field concerning the effectiveness of detecting and monitoring health attributes of individuals. The contributions made through the study have been discussed with an implication towards future research that can be carried out as a part of its application in a practical essence
Agentic AI-Driven Data Product for Automated Healthcare Insights Generation
This study implements the Agentic AI data product development and delivery process for healthcare appointment no-show prediction with agentic AI for autonomous insights generation post-implementation. The Healthcare Appointment Dataset was obtained from Kaggle, used to predict attendance for scheduled appointments versus no-shows. Notable variables include (but are not limited to) patient age, gender, scholarship attendance, and the number of days a patient was forced to wait (after requesting an appointment) for their appointment. The prediction was thus based on attendance (or not); the model produced an applicable output from a relevant research question with 77.33% accuracy and consistently significant results predictive of practical application within healthcare settings. Moreover, agentic AI was executed to facilitate insights generation independently based on the model results without researcher-induced intervention. The fields produced by the nature of insights discovered reveal that the higher average days of waiting time for the appointment they had, coupled with the lower average age of patients, the better predictive capacity for no-show status. Thus, performance prediction modeling accompanied by agentic insights generation for results assessment increases interpretability, efficacy, and decision support capabilities within the healthcare analytics world. Furthermore, future research will be conducted with a multimodal source reinforcement learning approach to improve prediction accuracy and real-time available metrics solidifying agentic AI within predictive healthcare endeavors for real-world applicability
Evaluating the Role of Real-Time Business Intelligence Dashboards in Enhancing Healthcare Performance Monitoring
This paper gives a description of the role of Business Intelligence (BI) tools in Healthcare Systems, fostering new innovation in the field of Healthcare Performance Monitoring process. Clinical and administrative problems of the healthcare sectors are impacted by BI tools, which is one of the primary objectives. In addition to this, the study suggests automation of administrative tasks, reduction of cost overheads and objectifying the areas that need to be improved. This also aims at the implementation of instrumental strategies that lead to efficient decision-making to determine the possible future workflow processes to take place. The study highlights a set of information and analytical aspects which determine the development and usability of the real-time dashboards and their application across the healthcare setting. The consideration of healthcare datasets and their implementation across an analytical dashboard is expected to help in enhancing the credibility of administrative bodies to undertake data-driven decisions. However, the issues that lie within its implementation might have a significant impact on the areas of data management, such that maximum compatibility can be attained while sustaining the scalability of operations. Insights into the changes that are revealed through the help of artificial intelligence help in programming the dashboards in such a way that visual insights provide an indication of the effectiveness of decisions that take place across a healthcare setting
The Role of Machine Learning for Detecting Malicious Internet Traffic
With the blistering development of the Internet, encrypted communication, cloud environments, and IoT systems, the magnitude and complexity of fraudulent network traffic have grown dramatically. Intrusion detection systems that rely on signature-based detection mechanisms are increasingly less effective due to the use of encryption, protocol obfuscation, and distributed device ecosystems by modern attackers to hide the malicious behaviour. With the increase in the heterogeneity and high-volume network environments, adaptive, behaviour-oriented mechanisms of detection have become paramount. The major difficulty is in the analysis of high-dimensional, highly encrypted, imbalanced, and distorted by sampling or incomplete visibility malicious traffic. Most network flows have finer behavioural deviations as opposed to explicit payload signatures. Further, IoT devices produce vast amounts of unreliable, resource-limited traffic and encrypted messages conceal content-based features. These circumstances compromise the performance of the conventional methods of detection and demand more sophisticated modelling strategies. The study focuses on critically reviewing how machine learning can be used to monitor malicious Internet traffic on general IP networks, cloud platforms, IoTs, and encrypted communication channels. The paper presents a synthesis of empirical findings of multiple machine-learning frameworks, such as flow-based classifiers, correlation-optimal IoT models, deep neural networks, multimodal encrypted-traffic models, and ensemble approaches to learning. The article measures the enhancement of machine learning in terms of accuracy, adaptability, imbalance sensitivity, and robustness under encryption by comparing performance based on detection. The article offers a concerted analytical evaluation of machine-learning-traffic detecting in 15 peer-reviewed studies; compares performance patterns in the cloud, IoT, and encrypted systems; detects the architectural and statistical variables that affect the accuracy of detection; exposes limitations, including sampling distortions and encryption opaque, and synthesises insights into a broad view of the process through which machine learning improves the detection of malicious Internet traffic in a changing network ecosystem
Smart Contract-Driven Consent Management for Personal Data Sharing
The rapid expansion of digital platforms, electronic health systems, IoT devices, and cross-organisational data-sharing environments have resulted in the exchanged amount and sensitivity of personal data growing. Conventional consent management models are centralised, non-transparent, and hard to audit, exposing threats of unauthorised distribution, poor interoperability, and substandard compliance with regulations. Traditional systems have a hard time delivering dynamic, fine-grained and verifiable user control over consent. They do not have transparent audit trails, do not support the use of multi-party authorization, and do not impose the use of data in a manner specific to purpose, particularly when regulated by laws like GDPR. The issue of scalability, the absence of automation, and immutable logging also contribute to the growth of trust and security concerns. The objective of the research is to assess in a critical manner the concept of blockchain-based and smart contract-based consent management models to determine the architectural designs, performance aspects, cryptographic techniques, and compliance measures that enhance personal data control within healthcare, fitness tracking, and wider data-sharing systems. Solution: This paper summarises the evidence regarding the benefits of hybrid on-chain/off-chain architectures, purpose-based access control, threshold cryptography, pseudonymization layers, and business-process-sensitive workflows in improving the transparency, auditability, and automation of consent management through the analytical review of nineteen blockchain-based consent systems. Smart contract systems give enforceability of rules, minimise risk of breach and enhance precision of consent revocation. The remaining issues are scalability, fluctuations in the cost of gas, GDPR-compatible deletion, and multidimensional approval. In general, consent systems based on smart contracts provide a technically plausible and legally consistent platform on which to build the systems of personal data-sharing in the future
Vision-Based Human Action Recognition Using Skeleton Graph Neural Networks
HAR is an essential computer vision task, which provides the machine with an automatic representation of human actions and human action classification on the basis of visual representations. Older methods of HAR are based on RGB or RGB-D videos, and, in spite of their success in controlled settings, they are prone to occlusions, variations in lighting, and a high level of computational complexity. HAR based on skeletons has been proposed as an alternative, a representation of human poses in terms of joints or keypoints and tracking both spatial and temporal behavior of movement. This abstraction enhances resiliency to visual noise, lowers computational costs, and supports real time usage. Graph Neural Networks (GNNs), and in particular Graph Convolutional Networks (GCNs), have proven to have a phenomenal success in skeleton-based HAR, by considering the human body as a spatial-temporal graph with joints representing nodes and bones or temporal relationships as edges. The paper provides a systematized review of the vision-based human action recognition based on skeleton GNNs through skeleton representation, graph construction and development, early and advanced GNN architecture, and how they are enhanced by adaptive graphs, attention, and hierarchical modeling. Some of the most critical issues, including skeleton noise, time mismatch, generalization across views, over-smoothing in deep GCNs, and computational complexity, are addressed, as well as solutions to these problems. Also, we emphasize surveillance, healthcare, human-computer interaction, and robotics. Lastly, the future research directions are suggested, which are multi-moding, lightweight models of edge devices, self-monitored learning, and cross-dataset generalization. The current review offers a solid background to researchers who want to come up with strong, effective, and understandable skeleton-based systems of HAR
Advanced Algorithms for Real-time Risk Forecasting
This white paper delves into the transformative role of advanced algorithms in enhancing real-time risk forecasting. In today’s fast-evolving technological and economic environment, the ability to swiftly anticipate and mitigate risks has become a strategic imperative. The paper traces the evolution of risk forecasting methodologies, contrasting traditional statistical models with state-of-the-art techniques powered by artificial intelligence and machine learning. It underscores the advantages of modern algorithms such as neural networks, decision trees, and ensemble methods in processing vast, dynamic datasets to deliver more accurate, adaptive, and timely predictions. These innovations are reshaping risk management across sectors including finance, cybersecurity, healthcare, and disaster response. By showcasing current advancements and their broad implications, this white paper aims to equip organizations with the insights needed to adopt predictive analytics for proactive risk management and informed strategic decision-making
Generative Scene Graphs for Explainable Perception in Autonomous Vehicles
Perception systems in autonomous and advanced driver assistance vehicles increasingly rely on large, data driven neural architectures that achieve strong accuracy but remain fundamentally opaque. Their internal reasoning is difficult to interpret, verify, or trace, which poses challenges for the safety certification, debugging, and regulatory transparency. Existing attempts at interpretable perception such as symbolic reasoning, attention visualization, or post hoc saliency rarely provide structured, causally meaningful explanations that planners, auditors, or human operators can reliably trust. This paper introduces a generative perception framework that produces a fully interpretable scene graph representation as the primary output rather than as an optional diagnostic layer. The scene graph encodes objects, semantic attributes, relations, interactions, and driving relevant affordances in a structured form compatible with downstream decision making and formal analysis. The proposed approach employs a generative model that operates in graph latent space to enforce global physical and semantic consistency. Instead of passively extracting relations, the model actively predicts missing, uncertain, or occluded components while maintaining adherence to vehicle dynamics constraints, traffic rules, and common sense priors learned from data. This generative mechanism allows the perception system to expose uncertainty at the node, relation, and affordance levels, enabling explicit traceability of potential failure modes. The resulting graph structure serves as both an interpretable explanation of system’s perception and a robust intermediate representation for planning. Experiments conducted on multi sensor autonomous driving datasets demonstrate that generative scene graphs substantially improve explanation quality and relational correctness, especially under occlusions and degraded sensing. At the same time, detection performance remains competitive with state of the art black box methods. By unifying generative modeling, relational reasoning, and structured explainability, this work positions generative scene graphs as a practical step toward transparent, auditable, and regulator aligned perception pipelines in autonomous vehicles
AI-Driven Detection of Fake News and Sentiment Analysis: A Framework for Truth Verification and Emerging Pattern Recognition
The rapid spread of misinformation on digital platforms poses a significant threat to societal stability and democratic discourse. This paper proposes an artificial intelligence (AI)-driven framework for fake news detection, sentiment analysis, and truth verification. The model integrates transformer-based natural language processing (NLP) architectures with graph-based pattern recognition to distinguish factual content from fictitious narratives. We also introduce a novel hybrid model combining contextual embeddings and graph topology learning to identify emerging patterns in information dissemination
AI-Driven Cross-Benefit Analytics: Optimizing Health and Pharmacy Plan Decisions for Value-Based Insurance Design
In the evolving landscape of value-based care, health insurers face growing pressure to align benefit designs with both clinical outcomes and cost-effectiveness. Traditional benefit structures often silo medical and pharmacy data, limiting the ability to holistically evaluate a member’s health journey. This paper proposes an AI-driven Cross-Benefit Analytics (CBA) framework that integrates medical claims, pharmacy data, and social determinants of health (SDOH) to support intelligent, data-driven decisions in insurance plan optimization. Using supervised machine learning and reinforcement learning models, we develop predictive engines capable of forecasting longitudinal health outcomes and downstream costs associated with various formulary and plan design configurations. This platform simulates the trade-offs of high-cost pharmaceutical interventions versus potential reductions in emergency visits or inpatient stays. We further demonstrate how the system can recommend optimal plan structures such as high-deductible versus managed care plans tailored to individual member risk profiles. Results from a large-scale commercial dataset illustrate ~18% potential reduction in total cost of care through AI-optimized benefit adjustments. The framework supports dynamic, transparent, and equitable value-based insurance design (VBID), providing actionable insights for payers, providers, and policy makers. Ethical considerations around model fairness, explainability, and regulatory compliance are also discussed to promote responsible AI integration in health insurance systems
Autonomous ETL Pipelines: Using Generative AI to Design, Validate, and Deploy Dataflows
The Extract, Transform, and Load (ETL) process, a cornerstone of data engineering, has traditionally been laborious, time-consuming, and error-prone, resulting in a substantial bottleneck in today's data-driven enterprises. This study investigates the paradigm shift toward autonomous ETL pipelines powered by Generative AI and Large Language Models (LLMs). Provides a comprehensive architectural framework in which prompt-driven AI agents analyze natural language needs to build pipeline logic, construct sophisticated schema mappings, and create infrastructure-as-code (IaC) templates for deployment. The study examines the integration of AI copilots into modern platforms such as Microsoft Fabric and Databricks, which enable not only initial code generation but also continuous validation of transformation logic, dynamic optimization, and self-healing of failed jobs via reinforcement learning feedback loops. The result is a scalable, adaptive dataflow system that requires minimal human intervention, dramatically increasing development speed, operational consistency, cost-efficiency, and overall observability across complex, hybrid data ecosystems. The report finishes with a discussion of the obstacles, including delusion, security, and expense, as well as future research possibilities for fully autonomous, trustworthy data management
Time-Series Analysis of Sensor Data for Smart Devices
The rapid influx of intelligent devices has generated enormous amounts of sensor data, with new possibilities for modeling device user behavior and device optimization through predictive maintenance and health monitoring. The thrust of this paper is in the application of time-series analysis methods to comprehend and model sensor data from intelligent devices with a focus on lean methods for low-resource settings. We discuss techniques for identifying patterns of user behavior and device usage, such as statistical models, machine learning models, and deep networks like LSTMs. We also discuss anomaly detection that can predict device failure and track health metrics to facilitate early intervention and maintenance. Examples of real-world applications like predictive maintenance of home automation systems and health monitoring with wearable sensors are presented. All major challenges like computational expense, data quality, and privacy are discussed, and future directions toward extending time-series analysis by using edge computing and future models. The paper concludes by showing the potential of time-series analysis to make devices efficient, enhance user experience, and real-time health monitoring